Hello, everyone, and thank you for joining today's session. My name is Siraj. We'll be exploring how containerization and Kubernetes have transformed the way we develop, deploy, manage applications in the cloud. This talk is especially important in the context of IOT, where number of devices is exploding, data volumes are skyrocketing. We'll see how these technologies tackle scaling challenges, improve efficiency, and enable rapid innovation, both in traditional IT environment. And the edge where IoT devices operate. Let's get started. we'll, discuss briefly about the containerization. what it means is like it packages an application code, libraries, and all dependencies together. Ensuring it runs the same way across different environment, development, testing, production. Think of container as a sealed box that includes everything your app needs to function. For IoT, this means software running on edge devices or gateways can be developed and tested on laptop, then deployed confidently in the field without compatibility issues. Popular tools include Docker, Podman, and CRM. So, like there are definitely a lot of advantages using containerization. the first one would be the portability. it helps us to move from on premises setup to cloud or edge environment effortlessly. And next is consistency. the application environment is identical everywhere, reducing it works on. My machine problem. What it means is like before containerization. The problem was like, once a developer developed any software, so it was working fine on his machine, but not on the real production environment. So the reason was there might be some mismatch in the libraries. So all those kind of issues will not happen here in dockerite or in containerization. And obviously they are lightweight. Okay. So they can start within seconds, enable quick updates and scaling, and coming to the scalability, it is easy, easily spin up more containers to handle additional load from surge in IoT sensors data during peak time, like festival or sporting event in the smart city. These benefits help IoT deployment evolve. rapidly without sacrificing stability or performance. So now, as as the containers are growing, as the applications are growing, the containers as well growing, right? So we need something to manage those containers. That's where Kubernetes come into play. So Kubernetes is the orchestration layer that automates managing containers at scale. It schedules, scales and maintain the health of containerized application. Removing a lot of manual overhead. For IoT scenarios, Kubernetes can coordinate multiple services from data ingestion at the edge to centralized analytics, ensuring that if one component fails, another is automatically restarted or replaced. Key features include self healing, horizontal scaling based on demand, And built in load balancing in the cloud. Kubernetes simplifies orchestration. Imagine trying to manage thousands of containers by hand. It's error prone and time consuming. Kubernetes automates scaling up and down as devices. As device numbers fluctuate, ensures resilience if hardware fails, work across different providers. For IoT data pipelines, Kubernetes can run analytics workload in different regions or clouds, preventing vendor lock in. This flexibility is crucial as IoT deployments scale globally. So yeah, let's discuss, innovation driven by containerization. So the first one is microservice architecture. Instead of large application, we'll break it into the small focus on services in iot that mean, that might mean separate services for data ingestion, separate service for pre-processing, and machine learning, inter interface. And efficient resource use. What it means is multiple services share the same infrastructure without conflicts. Crucial for resource constrained edge environment. and it helps in DevOps enablement. Rapid and continuous integration and deployment pipelines keep IoT software updated and secure. And it quickly test new IOT, it help us to like, definitely quickly test and deploy IOT features like anomaly detection on sensor data without worrying about environment setup. So Kubernetes is widely used across different domains. Enterprise applications like banks, healthcare system, retailers rely on Kubernetes for stable, scalable platforms. Kubernetes native development. So startup leverage Kubernetes to handle millions of concurrent users and tools like Spark run efficiently under Kubernetes helping analyze massive IoT data sets and it helped in AI and ML pipelines, model training and interface scale up automatically as data streams from IoT devices increases and IoT deployment. This is key for our audience today. As IoT devices proliferate, smart city sensors, industrial robots, autonomous vehicles, Kubernetes manages containerized services at scale. It can autoscale to handle Growth in connected device and data, bring processing closer to the edge for faster response time, essential in real time IoT scenarios like traffic control. Efficiently run multiple applications on the same hardware, saving resources, enable rolling updates of software on thousands of devices simultaneously. Support hybrid and multicloud strategies to process IoT data where it makes the most sense on prem. So let's discuss about a few case studies. So while not strictly IoT focus, so let's focus on this Spotify story shows the power of Kubernetes at scale. They needed to handle millions of concurrent users. By adopting Kubernetes, they transformed their monolithic setup into microservices. They gain the agility to roll out updates faster, improve user experience, and control costs. The same principles apply to IoT. As your device count and data volumes grow, Kubernetes helps maintain performance and reliability. Despite its benefits, Kubernetes isn't without challenges. Complexity. Kubernetes has learning curves. Engineers need training to use it effectively. And for monitoring and debugging, understanding what's happening in large IoT ecosystems spread across edge devices and cloud requires robust tools like Chrometheus, Grafana. And coming to the security, protecting IOT data is critical. Ensuring containers and clusters are secure and up to date is a constant effort. Cost management. So scaling too aggressively or running unused containers can inflate cloud bills. Careful planning ensures resource match actual IOT workload. In a world where number of connected IOT devices is exploding and data is flowing from countless sources, containerization and Kubernetes form a powerful foundation for managing complexity and scaling, scaling efficient, effectively. As IOT systems become more sophisticated, combining edge computing, AI and big data analytics, this technology ensures agility, resilience and cost effectiveness. Thanks. yeah, a small startup experiment with a small IoT application and scale it gradually. upskill your team. So invest in Kubernetes training so your engineers can confidently manage these environments. So consult with experts. If you are new to this, partner with consultants who can guide your IoT journey. Adopt best practices like follow security hardening. Monitoring and cost optimization strategies to realize the full potential of containers and communities in iot by embracing containerization and communities. You propose your iot project for long term success, handling growth, innovation, complexity with easy. for joining me today. Thanks.